Papers with news article
Automatic Detection of Entity-Manipulated Text using Factual Knowledge (2022.acl-short)
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| Challenge: | Current fake news detectors that exploit stylometric signals from the text are insufficient for distinguishing manipulated text from human written text. |
| Approach: | They propose a neural network detector that detects manipulated news articles by reasoning about the facts mentioned in the article. |
| Outcome: | The proposed detector outperforms the state-of-the-art detector in accuracy. |
Benchmarks and models for entity-oriented polarity detection (N18-3)
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| Challenge: | a dataset of 17,000 manually labeled documents is large for determining entity-oriented polarity in business news. |
| Approach: | They propose a convolutional neural network-based approach to classify entity-oriented polarity in business news. |
| Outcome: | The proposed model is based on convolutional neural networks and is small on the scale of existing models. |
Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)
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| Challenge: | Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule. |
| Approach: | They propose a graph neural network-based model which captures sentence interactions within a document. |
| Outcome: | The proposed model beats baselines and achieves state-of-the-art accuracy on existing datasets. |
Discourse Structures Guided Fine-grained Propaganda Identification (2023.emnlp-main)
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| Challenge: | Using teacher-predicted probabilities and knowledge distillation frameworks to identify propaganda content is important. |
| Approach: | They propose to integrate local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively. |
| Outcome: | The proposed models improve accuracy and recall of propaganda content identification at sentence-level and token-level. |
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection (D19-50)
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| Challenge: | A shared task on fine-grained propaganda detection was organized at EMNLP-IJCNLP 2019 . 12 systems submitted systems for the FLC task, 25 for the SLC task, and 14 teams submitted a system description paper . |
| Approach: | They present a task on fine-grained propaganda detection as part of the NLP4IF workshop at EMNLP-IJCNLP 2019 . they used a corpus of news articles annotated with an inventory of propagandist techniques at the fragment level to determine the propaganda technique used in each fragment . |
| Outcome: | The shared task on fine-grained propaganda detection was organized at the EMNLP-IJCNLP 2019 . 12 systems submitted for the FLC task, 25 for the SLC task, and 14 submitted a system description paper . |
Author’s Sentiment Prediction (2020.coling-main)
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| Challenge: | Existing work on inferring author sentiment in news articles hasn't been done on this domain. |
| Approach: | They propose a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. |
| Outcome: | The proposed dataset performs the best amongst the baselines, but only achieves modest performance overall suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. |
“Let’s not Quote out of Context”: Unified Vision-Language Pretraining for Context Assisted Image Captioning (2023.acl-industry)
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| Challenge: | Large enterprises have several teams to create their content for the purpose of marketing, campaigning, or even maintaining a brand presence. |
| Approach: | They propose a new unified Vision-Language (VL) model with a focus on context-assisted image captioning where the caption is generated based on both the image and its context. |
| Outcome: | The proposed model achieves state-of-the-art with an improvement of up to 8.34 CIDEr score on the benchmark news image captioning datasets. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
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| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |
A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News (2021.eacl-main)
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| Challenge: | Exaggerations in health news can have tremendous adverse effects on the lifestyle of the common masses who feed themselves mostly on such news instead of the source scientific publication. |
| Approach: | They propose a three-step approach that extracts relation phrases from a scientific paper and then classifies the strength of the relationship phrase extracted. |
| Outcome: | The proposed approach outperforms baseline models that compare state-of-the-art embedding of the statement pairs through a binary classifier or recast the problem as a textual entailment task. |
A Joint Model for Structure-based News Genre Classification with Application to Text Summarization (2021.findings-acl)
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| Challenge: | Existing models for structure-based news genre classification identify news structure types and news elements . authors show that the model outperforms variants that perform two tasks independently . |
| Approach: | They propose a joint model that identifies one of four commonly used news structures for a news article and recognizes a sequence of news elements within the article that define the corresponding news structure. |
| Outcome: | The proposed model outperforms variants that perform two tasks independently . it predicts news structure type and news elements and improves text summarization . |
Focus! Relevant and Sufficient Context Selection for News Image Captioning (2022.findings-emnlp)
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| Challenge: | Recent work only coarsely leverages the article to extract the necessary context, which makes it difficult for models to identify relevant events and named entities. |
| Approach: | They propose to use a vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model. |
| Outcome: | The proposed model significantly improves on existing models and achieves state-of-the-art on multiple benchmarks. |
Distinguishing Between Foreground and Background Events in News (2020.coling-main)
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| Challenge: | a new task is needed to distinguish between foreground and background events in news articles . |
| Approach: | They propose a task of distinguishing between foreground and background events in news articles . they also identify the general temporal position of background events relative to the foregoing period . |
| Outcome: | The proposed model achieves good performance on a dataset of news articles . |
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020.acl-main)
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| Challenge: | Unmeasured or latent confounders can bias causal estimates and this has motivated interest in measuring potential confounder from observed text. |
| Approach: | They propose to use text to measure potential confounders in a way that allows for a rich measurement of multiple confounder variables. |
| Outcome: | The proposed method is based on an individual’s entire history of social media posts or the content of a news article. |
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation (2020.emnlp-main)
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| Challenge: | Existing methods for news headline generation focus on producing a single short sentence . et al., 2017; Gehrmann e.t., 2018; Zhong ee., 2019) focus on single-headline generation. |
| Approach: | They propose a method to generate multiple headlines with keyphrases of user interests . they propose generating multiple keyphrase-relevant headlines using a transformer decoder . |
| Outcome: | The proposed method achieves state-of-the-art in terms of quality and diversity. |
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)
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| Challenge: | Existing methods for news comment generation have not been well studied. |
| Approach: | They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network. |
| Outcome: | The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets. |
Identifying Conspiracy Theories News based on Event Relation Graph (2023.findings-emnlp)
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| Challenge: | Conspiracy theories are narratives that explains an event or situation in an irrational or malicious manner. |
| Approach: | They propose to integrate an event relation graph into conspiracy theory identification by using soft labels. |
| Outcome: | The proposed approach improves precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources. |
Exploring the Impact of Vision Features in News Image Captioning (2023.findings-acl)
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| Challenge: | Recent state-of-art models can achieve competitive performance even without vision features. |
| Approach: | They conduct extensive experiments with mainstream news image captioning models to determine whether vision features contribute to the generation of captions. |
| Outcome: | The proposed models can achieve competitive performance even without vision features. |
NarratEX Dataset: Explaining the Dominant Narratives in News Texts (2025.findings-emnlp)
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Nuno Guimarães, Purificação Silvano, Ricardo Campos, Alipio Jorge, Ana Filipa Pacheco, Dimitar Iliyanov Dimitrov, Nikolaos Nikolaidis, Roman Yangarber, Elisa Sartori, Nicolas Stefanovitch, Preslav Nakov, Jakub Piskorski, Giovanni Da San Martino
| Challenge: | a dataset is created to explain the choice of the dominant narrative in a news article . the dataset is intended to address discourse polarization and propaganda detection . |
| Approach: | They propose a dataset for explaining the choice of the dominant narrative in a news article . the dataset is annotated manually with a dominant narrative and sub-narrative labels . |
| Outcome: | The proposed dataset is designed to explain the choice of the dominant narrative in a news article. |